Intelligent Setting and Model Predictive Control of Wastewater Treatment Process Based on Case Search Optimization

To address the challenges in wastewater treatment processes–namely, the multi-objective conflict between effluent quality and operational energy consumption, complex dynamic characteristics, and the difficulty of applying traditional optimization methods online–this paper proposes an intelligent setpoint method based on case search optimization. This method constructs an optimization framework of "offline construction and online decision-making." In the offline phase, it uses Kmeans++ to cluster historical operating conditions and employs NSGA-II to screen Pareto-optimal solutions, thereby building a knowledge base. In the online phase, it retrieves optimal setpoints through operating condition matching and similarity calculation. By integrating a dynamic case library maintenance strategy with an Echo State Network-based Model Predictive Controller, the method achieves precise setpoint tracking. Simulation results on the BSM1 platform demonstrate that, while ensuring effluent quality meets discharge standards, this method reduces operational energy consumption by approximately 3.9% compared to the traditional fixed setpoint strategy. It achieves an intelligent dynamic balance between water quality and energy consumption, providing an efficient and reliable new approach for the optimal operation of wastewater treatment processes.

Keywords: Wastewater treatment, Case search optimization, Multi-objective optimization, Setpoint optimization, Model predictive controlnstruction.